Runoff sensitivity to spatial rainfall variability: A hydrological modeling study with dense rain gauge observations

Abstract. Precipitation is a key input to hydrological models. While rain gauges provide the most direct precipitation measurements, their accuracy in capturing rain patterns highly depends on the spatial variability of rainfall events and the gauge network density. In this study, we employ a high-resolution meteorological station network (mean station distance of 1.4 km), the WegenerNet in southeastern Austria, to investigate the impact of station density and interpolation schemes on runoff simulations. We first simulate runoff during heavy precipitation (three short-duration and three long-duration events) using a physically based hydrological model with precipitation input obtained from a full network of 158 stations. The same simulations are then repeated with precipitation inputs from subnetworks of 5, 8, 16, 32, and 64 stations, using three different interpolation schemes – Inverse Distance Weighting with a weighting power of 2 and of 3, respectively, and Thiessen polygon interpolation. We find that the performance of runoff simulations is greatly influenced by the spatial variability of precipitation input, especially for short-duration rainfall events and in small catchments. For long-duration events, reliable runoff simulations in the study area can be obtained with a subnetwork of 16 or more well-distributed gauges (mean station distance of about 6 km). We find a clear effect of interpolation schemes on runoff modeling as well, but only for low-density gauge networks. The sensitivity to the precipitation input is smaller for long-duration heavy precipitation events and bigger catchments. As a next step we suggest to study an ensemble of precipitation datasets in combination with runoff modeling to be able to decompose the effects of precipitation measurement uncertainties and its spatial variability.